David Blanc Brioir
d2f7165120
- Add complete Library RAG application (Flask + MCP server) - PDF processing pipeline with OCR and LLM extraction - Weaviate vector database integration (BGE-M3 embeddings) - Flask web interface with search and document management - MCP server for Claude Desktop integration - Comprehensive test suite (134 tests) - Clean up root directory - Remove obsolete documentation files - Remove backup and temporary files - Update autonomous agent configuration - Update prompts - Enhance initializer bis prompt with better instructions 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
92 lines
2.4 KiB
Python
92 lines
2.4 KiB
Python
#!/usr/bin/env python3
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"""
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Exemple d'utilisation DIRECTE du pipeline PDF (sans MCP).
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Plus simple et plus de contrôle sur les paramètres!
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"""
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from pathlib import Path
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from utils.pdf_pipeline import process_pdf, process_pdf_bytes
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import weaviate
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from weaviate.classes.query import Filter
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def example_process_local_file():
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"""Traiter un fichier local (PDF ou Markdown)."""
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result = process_pdf(
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pdf_path=Path("md/peirce_collected_papers_fixed.md"),
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output_dir=Path("output"),
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# Paramètres personnalisables
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skip_ocr=True, # Déjà en Markdown
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use_llm=False, # Pas besoin de LLM pour Peirce
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use_semantic_chunking=False, # Chunking basique (rapide)
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ingest_to_weaviate=True, # Ingérer dans Weaviate
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)
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if result.get("success"):
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print(f"✓ {result['document_name']}: {result['chunks_count']} chunks")
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print(f" Coût total: {result['cost_total']:.4f}€")
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else:
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print(f"✗ Erreur: {result.get('error')}")
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def example_process_from_url():
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"""Télécharger et traiter depuis une URL."""
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import httpx
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url = "https://example.com/document.pdf"
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# Télécharger
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response = httpx.get(url, follow_redirects=True)
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pdf_bytes = response.content
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# Traiter
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result = process_pdf_bytes(
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file_bytes=pdf_bytes,
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filename="document.pdf",
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output_dir=Path("output"),
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# Paramètres optimaux
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use_llm=True,
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llm_provider="mistral", # Ou "ollama"
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use_semantic_chunking=True,
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ingest_to_weaviate=True,
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)
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return result
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def example_search():
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"""Rechercher directement dans Weaviate."""
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client = weaviate.connect_to_local()
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try:
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collection = client.collections.get('Chunk')
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# Recherche sémantique
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response = collection.query.near_text(
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query="nominalism and realism",
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limit=10,
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)
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print(f"Trouvé {len(response.objects)} résultats:")
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for obj in response.objects[:3]:
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props = obj.properties
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print(f"\n- {props.get('sectionPath', 'N/A')}")
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print(f" {props.get('text', '')[:150]}...")
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finally:
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client.close()
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if __name__ == "__main__":
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# Choisir un exemple
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# example_process_local_file()
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# example_process_from_url()
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example_search()
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